Jarque-Bera test for normality

Applies the Jarque-Bera goodness-of-fit test for normality (Jarque & Bera, 1980) to a one-dimensional signal: the statistic \(JB = \frac{N}{6}\left(S^2 + \frac{K^2}{4}\right)\), where \(S\) is the skewness and \(K\) the excess kurtosis (from signalMoments), is compared to a chi-squared distribution with 2 degrees of freedom. It is the formal, moment-based counterpart of the descriptive near-Gaussian check: a large statistic rejects normality. Same definition as scipy.stats.jarque_bera.

Usage

jarqueBeraTest(x)

Arguments

x

A numeric vector (the time series).

Value

A list with statistic (the JB statistic), p_value (the chi-squared upper-tail probability), and df (2).

References

Jarque, C.M., Bera, A.K. (1980). Efficient tests for normality, homoscedasticity and serial independence of regression residuals. Economics Letters, 6(3), 255-259.

See also

signalMoments

Examples

set.seed(1)
jarqueBeraTest(rnorm(1000))$p_value   # Gaussian -> large p (fail to reject)
#> [1] 0.9697854